The term neural network often refers to artificial neural networks, which are composed of networked neurons or nodes. The term may refer to either biological neural networks or artificial neural networks for solving artificial intelligence problems.
A neural network is an information processing paradigm inspired by the way biological neural systems process data. The intent of many neural network networks is to be able to replicate the functional abilities of a biological neural network, which is typically composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive.
It would be of great benefit to be able to implement an artificial neural network. The artificial neural networks may be used for many purposes. For example, neural networks can be used to perform predictive modeling, adaptive control, and many other types of analytical applications.
There have been many attempts in the past to use computing technologies to implement neural networks. However, past approaches to use computers failed to achieve desired performance levels because computers are just not sophisticated or complex enough to effectively replicate biological neural networks.
Therefore, there is a need for an improved approach to implement artificial neural networks that provides usable performance capabilities, and which can effectively replicate the sophistication and complexity of biological neural networks.